Devices and methods for fall detection based on phase segmentation

ABSTRACT

Aspects of the subject matter described in this disclosure can be implemented in a fall detection device and method. One or more motion sensors can access a user&#39;s acceleration data. The acceleration data can be segmented using a segmentation algorithm to identify a potential fall event. The segmentation algorithm can determine a cumulative sum of the acceleration data, where the cumulative sum is based on acceleration values being greater than or less than an acceleration threshold value, and a potential fall event can be identified where the cumulative sum is greater than a cumulative sum threshold value. Statistical features can be extracted from the segmented acceleration data and aggregated, and a determination can be made as to whether the potential fall event is a fall event based at least in part on the statistical features.

TECHNICAL FIELD

This disclosure relates generally to fall detection devices and methods,and more particularly, to accurately detecting falls based on asegmentation algorithm using data gathered from one or more motionsensors.

DESCRIPTION OF RELATED TECHNOLOGY

Falls represent one of the biggest health risks for many people,especially the elderly. Approximately one in every three adults above 65years of age fall each year, and approximate one in every two adultsabove 80 years old fall each year. Falls are the fifth leading cause ofdeath over age 65, and about 10% of falls lead to major injury such asfractures and brain trauma hospitalization. Fast discovery of fallevents can be critical in reducing injury and possibly preventing death.Fall detection devices are available, but many of them are not reliableand/or lack the ability to accurately detect a fall event.

Some conventional fall detection devices have the user press a callbutton to indicate a fall. However, in many instances of a fall, theuser may become unconscious or may suffer from a severe injury. Someconventional fall detection devices send a signal when the person'sorientation has changed to a horizontal position. However, suchsolutions produce too many false alarms or miss actual falls where theending position is not horizontal, such as falls happening on stairs.Some fall detection devices use sophisticated cameras and special imageanalysis algorithms to determine whether a person has fallen in a roomor area. However, image-based solutions suffer from problems such asprivacy concerns and difficulty in effectively monitoring all areaswhere a fall may take place. Some conventional fall detection devicesuse data from motion sensors, including accelerometers and/or tiltsensors, to determine if acceleration values are above or below simplethreshold values. However, while such fall detection devices may be wornby a user, they may implement motion sensors and algorithms that are notable to accurately and consistently distinguish between activities ofdaily living against actual falls. Such existing fall detection devicesmay produce false positives (i.e., identifying non-falls as falls),which can be costly, and may also produce false negatives (i.e., missingactual falls), which can be detrimental.

SUMMARY

The systems, methods and devices of this disclosure each have severalaspects, no single one of which is solely responsible for the desirableattributes disclosed herein.

One aspect of the subject matter described in this disclosure can beimplemented in a method of identifying a fall event. The method includesaccessing acceleration data of an electronic device from one or moremotion sensors and segmenting the acceleration data to identify apotential fall event, which includes determining a cumulative sum forthe acceleration data and identifying the potential fall event if thecumulative sum is greater than a cumulative sum threshold value. Themethod further includes extracting one or more statistical features fromthe segmented acceleration data and determining whether the potentialfall event is a fall event based at least in part on the one or morestatistical features.

In some implementations, segmenting the acceleration data includessegmenting the acceleration data into at least three phases, where theat least three phases include (i) a before-fall phase, (ii) aduring-fall phase, and (iii) an after-fall phase, where the during-fallphase includes the potential fall event. In some implementations, themethod further includes analyzing the duration of time of theduring-fall phase, where determining whether the potential fall event isa fall event is based at least in part on the duration of time of theduring-fall phase. In some implementations, the one or more statisticalfeatures include one or more of the following: variance, maximum,minimum, 75^(th) percentile, 25^(th) percentile, mean, median, meancrossing rate, absolute area, or any combination thereof. In someimplementations, the one or more statistical features include a meancrossing rate of acceleration extracted from the during-fall phase andvariance of acceleration extracted from the after-fall phase. In someimplementations, the electronic device is a wearable device. In someimplementations, the one or more motion sensors consist of one or moreaccelerometers. In some implementations, the cumulative sum is based onacceleration values greater than or less than the first accelerationthreshold value. The first acceleration threshold value and thecumulative sum threshold value are identified by a machine-learningalgorithm. In some implementations, determining whether a potential fallevent is a fall event includes aggregating the one or more statisticalfeatures from the segmented acceleration data, calculating a metricbased on the one or more statistical features, and classifying thepotential fall event as a fall event based at least in part on thecalculated metric.

Another aspect of the subject matter described in this disclosure can beimplemented in an electronic device for identifying a fall event. Theelectronic device includes one or more motion sensors configured toaccess acceleration data of the electronic device, a processor coupledto the one or more motion sensors, where the processor is configured tosegment the acceleration data to identify a potential fall event,extract one or more statistical features from the segmented accelerationdata, and determine whether the potential fall event is a fall eventbased at least in part on the one or more statistical features, wherethe processor configured to segment the acceleration data is configuredto determine a cumulative sum for the acceleration data and identify apotential fall event if the cumulative sum is greater than a cumulativesum threshold value.

In some implementations, the processor configured to segment theacceleration data is configured to segment the acceleration data into atleast three phases, where the at least three phases include (i) abefore-fall phase, (ii) a during-fall phase, and (iii) an after-fallphase, where the during-fall phase includes the potential fall event. Insome implementations, the one or more statistical features include amean crossing rate of acceleration extracted from the during-fall phaseand variance of acceleration extracted from the after-fall phase. Insome implementations, the electronic device is a wearable device and theone or more motion sensors consist of one or more accelerometers. Insome implementations, the electronic device further includes a low-passfilter configured to smooth out the acceleration data accessed from theone or more motion sensors.

Another aspect of the subject matter described in this disclosure can beimplemented in an electronic device for identifying a fall event. Theelectronic device includes means for accessing acceleration data andmeans for analyzing the acceleration data coupled to the means foraccessing acceleration data, the means for analyzing the accelerationdata configured to segment the acceleration data to identify a potentialfall event, extract one or more statistical features from the segmentedacceleration data, and determine whether the potential fall event isfall event based at least in part on the one or more statisticalfeatures, where the means for analyzing the acceleration data configuredto segment the acceleration data is configured to determine a cumulativesum for the acceleration data and identify a potential fall event if thecumulative sum is greater than a cumulative sum threshold value.

In some implementations, the means for analyzing the acceleration dataconfigured to segment the acceleration data is configured to segment theacceleration data into at least three phases, where the at least threephases include (i) a before-fall phase, (ii) a during-fall phase, and(iii) an after-fall phase, where the during-fall phase includes thepotential fall event, and where the one or more statistical featuresinclude: a mean crossing rate of acceleration extracted from theduring-fall phase and variance of acceleration extracted from theafter-fall phase. In some implementations, the electronic device is awearable device and the one or more motion sensors consist of one ormore accelerometers.

Details of one or more implementations of the subject matter describedin this disclosure are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings and the claims. Note thatthe relative dimensions of the following figures may not be drawn toscale.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flow diagram illustrating an example process foridentifying a fall event according to some implementations.

FIG. 2 shows a flow diagram illustrating an example process forsegmenting acceleration data to identify a potential fall eventaccording to some implementations.

FIG. 3 shows a flow diagram illustrating an example process fordetermining whether a potential fall event is a fall event based atleast in part on one or more extracted statistical features according tosome implementations.

FIG. 4A shows an example plot of acceleration data as a function oftime.

FIG. 4B shows an example plot of a cumulative sum as a function of time.

FIG. 4C shows an example plot of the acceleration data aftersegmentation.

FIG. 4D shows a magnified view of the example plot of the accelerationdata as a function of time in FIG. 4A.

FIG. 4E shows a magnified view of the example plot of the cumulative sumas a function of time in FIG. 4B.

FIG. 5 shows a block diagram representation of components of an examplefall detection device according to some implementations.

FIG. 6 shows a block diagram representation of a processor in a falldetection device and associated steps performed by the processoraccording to some implementations.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

The following description is directed to certain implementations for thepurposes of describing various aspects of this disclosure. However, aperson having ordinary skill in the art will readily recognize that theteachings herein can be applied in a multitude of different ways.Various embodiments will be described in detail with reference to theaccompanying drawings. References made to particular examples andimplementations are for illustrative purposes, and are not intended tolimit the scope of the claims.

The described implementations may be implemented in any device,apparatus, or system that includes a sensor system. In addition, it iscontemplated that the described implementations may be included in orassociated with a variety of electronic devices such as, but not limitedto: mobile telephones, multimedia Internet enabled cellular telephones,mobile television receivers, wireless devices, smartphones, smart cards,wearable devices such as bracelets, armbands, wristbands, rings,headbands, patches, belts, etc., Bluetooth® devices, personal dataassistants (PDAs), wireless electronic mail receivers, hand-held orportable computers, netbooks, notebooks, smartbooks, tablets, globalnavigation satellite system (GNSS) receivers/navigators, cameras,digital media players (such as MP3 players), camcorders, game consoles,wrist watches, electronic reading devices (e.g., e-readers), mobilehealth devices, etc. By way of example, the described implementationsmay be implemented in a wearable device. Some implementations of thewearable device may be a health-monitoring device worn by a user.However, the wearable device may have other capable of otherapplications, such as making/receiving phone calls,transmitting/receiving text messages, transmitting/receiving emails,keeping time, performing navigation, playing music, etc. Thus, theteachings are not intended to be limited to the implementations depictedsolely in the Figures, but instead have wide applicability as will bereadily apparent to one having ordinary skill in the art.

This disclosure relates generally to devices and methods for identifyinga fall event. A fall detection device can include one or more motionsensors for collecting a user's acceleration data over time. Theacceleration data can be processed and analyzed by applying asegmentation algorithm. The segmentation algorithm identifies apotential fall in the acceleration data, and segments the accelerationdata into one or more phases, such as: (1) a before-fall phase, (2) aduring-fall phase, and (3) an after-fall phase. Statistical features areextracted from each of the phases or from at least some of the phases,where examples of statistical features include: variance, maximum,minimum, 75^(th) percentile, 25^(th) percentile, mean, median, meancrossing rate, absolute area, and combinations thereof. The statisticalfeatures from at least some of the phases are aggregated and thepotential fall event is classified as a fall event using a classifiertrained by a machine-learning algorithm.

Particular implementations of the subject matter described in thisdisclosure can be implemented to realize one or more of the followingpotential advantages. The fall detection device and method of thepresent disclosure can more accurately and reliably distinguish genuinefall events from activities of daily living. This reduces the likelihoodof producing false positives and minimizes the risk of producing falsenegatives. Specifically, the present disclosure can detect genuine fallevents by analyzing statistical features across different phases, ratherthan analyzing one statistical feature or multiple statistical featuresin a smaller or unsegmented window. That way, instead of just looking atan unsegmented window encompassing a possible fall event, statisticalfeatures from a before-fall phase and statistical features from anafter-fall phase can provide additional valuable information fordetermining whether a genuine fall event occurred. Moreover, thesegmentation algorithm can further increase accuracy and reliability bysegmenting acceleration data of a user into the different phases with ahigh level of precision. The segmentation algorithm can more accuratelyand reliably identify possible fall events by using a cumulative sum ofacceleration data and comparing the cumulative sum to threshold values,rather than using acceleration data and comparing the acceleration datato threshold values. A machine-learning algorithm can be implemented inthe present disclosure to train a classifier and more accuratelyclassify a possible fall event as a genuine fall event. Whereas theaccuracy of conventional fall detection devices can be less than 95%,the accuracy of the fall detection device of the present disclosure canbe greater than 95%, or even greater than 98%. In some implementations,the present disclosure may reduce power consumption by collectingacceleration data without collecting or analyzing angular velocity,orientation, image data, or other monitoring data that can consume powerin a fall detection device. In some implementations, the fall detectiondevice can be a wearable device that can improve convenience andtracking with the user. The wearable device can be noninvasive,nonrestrictive to ambulatory use, and enable continuous monitoring ofthe user's activities.

FIG. 1 shows a flow diagram illustrating an example process foridentifying a fall event according to some implementations. The blocksof the process 100 are not necessarily performed in the order indicated.Moreover, the blocks of the process 100 may include more or fewer blocksthan shown and/or described. Although some blocks of the process 100 aredescribed as being performed by a single processor, in someimplementations more than one processor may be involved in performingthese operations. For example, more than one processor of a controlsystem may be involved in performing these operations. In someimplementations, the blocks of the process 100 can be performed by adevice shown in FIG. 5, or by a device shown in FIG. 6, as describedbelow. In some implementations, the blocks of the process 100 may beimplemented, at least in part, according to software stored on one ormore non-transitory computer readable media.

At block 105 of the process 100, acceleration data of an electronicdevice is accessed from one or more motion sensors. The electronicdevice may be associated with a user of the electronic device. The oneor more motion sensors can include one or more accelerometers. The oneor more accelerometers can be, for example, three linear accelerometersthat measure linear acceleration data along a particular orthogonal axisof a Cartesian coordinate system or a single three-dimensionalaccelerometer. In some implementations, the one or more motion sensorsconsist only of one or more accelerometers, which can improve powersavings. In some implementations, the one or more motion sensors can bepart of a wearable device, such as a wrist-worn wearable device.

In some implementations, additional data may be accessed from the one ormore motion sensors, such as data regarding the user's angularacceleration, angular velocity, rotation, magnetic field, force,orientation, and the like. In some implementations, the process 100 canfurther include accessing angular velocity data and orientation datafrom the one or more motion sensors. For example, the angular velocitydata and the orientation data may be accessed from one or moregyroscopes and one or more magnetometers. Such data can also besubsequently segmented and analyzed along with the acceleration data ofthe user.

The acceleration data, also referred to as “linear acceleration data,”“motion data,” “acceleration curve,” or “accelerometer reading,”provides the linear acceleration of the user over time. In someimplementations, the acceleration data can be invariant of direction.For example, the acceleration magnitude in the acceleration data isindependent of the linear acceleration along a particular orthogonalaxis of a Cartesian coordinate system (e.g., a=√(a_(x) ²+a_(y) ²+a_(z)²). As a result, falls can be detected regardless of whether someone isfalling backwards, forwards, sideways, etc. When there is no motion, theacceleration magnitude in the acceleration data may only show the forceof gravity. Variations in acceleration magnitudes can be attributable toroutine motion, such as walking, running, sitting down, picking up anobject, swinging arms, etc.

Falls can generally be characterized as uncontrolled downward movementof a user from a higher position to a lower position. Falls can happenin a number of ways. Examples include tripping or stumbling whilewalking forward, shifting of body weight while sitting down, shifting ofbody weight while getting up, shifting of body weight while walkingforward, shifting of body weight while initiating walking, hitting orbumping while standing, loss of consciousness while walking forward,loss of support with an external object during sitting down, loss ofsupport with an external object while getting up, etc. When people fall,they usually go into free fall until at least a portion of their bodyimpacts the ground, and they reach a stable equilibrium.

In some implementations, the acceleration data can be processed by afilter. Since cumulative motion of a user is mostly low in frequency, alow-pass filter can filter out acceleration values that fluctuatesignificantly. The filter can smooth out the acceleration data. Inaddition, the acceleration data can account for the acceleration of theuser by subtracting out the force of gravity. That way, what isidentified in the acceleration data is linear acceleration of the user.FIG. 4A shows an example plot of acceleration data as a function oftime. The plot includes normalized acceleration data after beingprocessed by a low-pass filter, and also includes normalizedacceleration data after being processed by a low-pass filter andsubtracting out the force of gravity. In FIG. 4A, the user exhibitsmovement including some variations of motion for the first few seconds,followed by several localized spikes in acceleration in the next fewseconds, and followed by a stable equilibrium without variations inacceleration in the remaining seconds. The localized spikes inacceleration may reflect the uncontrolled movement of the user as wellas the force of impact. The acceleration data in FIG. 4A may indicate apotential fall event.

Typical fall detection devices that implement motion sensors can focusalmost exclusively on the abrupt localized spikes in acceleration todetect fall events. In addition or in the alternative, typical falldetection devices analyze the acceleration data across a data windowwithout segmenting into different phases. In addition or in thealternative, typical fall detection devices may utilize other sensorsfor data collection, such as gyroscopes and orientation sensors, whichcan significantly add to the power consumption of the fall detectiondevice.

At block 110 of the process 100, the acceleration data is segmented toidentify a potential fall event, which includes a cumulative sum for theacceleration data being determined and the potential fall event beingidentified if the cumulative sum is greater than a cumulative sumthreshold value. In some implementations, the acceleration data issegmented into at least three phases. Each phase can represent a windowof time in the acceleration data. The at least three phases can include:(1) a before-fall phase, (2) a during-fall phase, and (3) an after-fallphase. The acceleration data accessed by the one or more motion sensorsat block 105 can be segmented using a segmentation algorithm. As aresult, the acceleration data can be precisely analyzed in differentsegments. Segmentation of the acceleration data using a segmentationalgorithm is described further in a process 200 in FIG. 2.

FIG. 2 shows a flow diagram illustrating an example process forsegmenting acceleration data to identify a potential fall eventaccording to some implementations. The process 200 may be performed in adifferent order or with different, fewer, or additional operations. Theblocks of the process 200 may be performed by one or more processors. Insome implementations, the blocks of the process 200 may be performed bya device shown in FIG. 5, or by a device shown in FIG. 6, as describedbelow. In some implementations, the blocks of the process 200 may beimplemented, at least in part, according to software stored on one ormore non-transitory computer readable media.

An example segmentation algorithm of the process 200 computes acumulative sum (CUSUM) according to a CUSUM algorithm. At block 205 ofthe process 200, a cumulative sum for the acceleration data isdetermined. In some implementations, the cumulative sum can be based onacceleration values being greater than or less than a first accelerationthreshold value. In some implementations, a first acceleration thresholdvalue (A_(th1)) and a cumulative sum threshold value (C_(th)) areidentified. One or both of A_(th1) and C_(th) are values that can beincorporated in the CUSUM algorithm. These threshold values are notarbitrarily determined, but may be determined from data-driven analysis.For example, these threshold values may be identified by conducting orgathering data from a series of fall events. In some implementations,A_(th1) and C_(th) may be identified from a machine-learning algorithm.In some implementations, the process 200 may further include identifyinga second acceleration threshold value (A_(th2)). In someimplementations, the cumulative sum can also be based on accelerationvalues being less than the second acceleration threshold value.

Acceleration values are cumulatively summed together, where accelerationvalues greater than A_(th1) increase the CUSUM values and accelerationvalues less than A_(th1) or less than A_(th2) decrease the CUSUM values.Rather than doing an instantaneous decision whenever an accelerationvalue is higher than a threshold to determine if a fall event hasoccurred or not, the CUSUM algorithm takes into consideration the sum ofacceleration values, where the sum is increased for acceleration valueshigher than A_(th1).

The CUSUM algorithm may be better understood by considering the examplesin FIGS. 4A-4E. In FIG. 4A, a first curve 402 represents an accelerationnorm after low-pass filtering (LPF). A second curve 404 in FIG. 4Arepresents absolute values of the difference between the accelerationnorm and the gravitational acceleration after low-pass filtering. Asused herein, values of the second curve 404 are referred to as linearacceleration values. In this example, the quantities represented in thesecond curve 404 are used for the determination of CUSUM according tothe disclosed implementations. Here, a first acceleration thresholdvalue can be set to 2.5. During the interval from 2 seconds to 3seconds, some of the linear acceleration values greater than 2.5 in thesecond curve 404 are greater than 2.5, and thus, a positive value (i.e.,a difference between the linear acceleration value and the firstacceleration threshold value) is added to respective CUSUM values. Arespective CUSUM curve 406 is shown in FIG. 4B. In contrast, after 3seconds, some of the linear acceleration values fall below 2.5, andthus, negative values (i.e., a difference between the linearacceleration value and the first acceleration threshold value) areaccumulated to respective CUSUM values. However, CUSUM does not go belowzero, and thus, it is clipped at zero when the CUSUM value becomesnegative as shown in FIG. 4B.

FIG. 4D shows a magnified view of the curves 402, 404 in FIG. 4A between2 seconds and 3 seconds, and FIG. 4E shows a magnified view of the CUSUMcurve 406 in FIG. 4B between 2 seconds and 3 seconds. To illustrate, ifthe CUSUM algorithm is represented by the formula: CUSUM[i] =max(0,CUSUM[i−1]+acceleration[i]−A_(th1)), where CUSUM[0]=0 and A_(thb)=2.5for the second curve 404 of FIGS. 4A and 4D, then the CUSUM curve 406can be represented in FIGS. 4B and 4E. At point 411 in FIG. 4D, thelinear acceleration value is greater than 2.5 by about 0.5. If aprevious respective CUSUM value for CUSUM[i−1] was 22.5, then 0.5 isadded to 22.5 so that the CUSUM curve 406 reaches a value of 23.0 atpoint 421 in FIG. 4E. At point 412 in FIG. 4D, the linear accelerationvalue is less than 2.5 by about 1.0. Assuming the previous respectiveCUSUM value for CUSUM[i−1] was 23.0 at point 421, then 1.0 is subtractedfrom 23.0 so that the CUSUM curve 406 reaches a value of 22.0 at point422 in FIG. 4E. The CUSUM curve 406 can increase or decrease dependingon the difference between the linear acceleration value and A_(th1),which adds to or subtracts from the previous CUSUM value. At point 413of the second curve 404, the difference between the linear accelerationvalue and A_(th1) is about 7.5, which is added to a previous respectiveCUSUM value to increase the CUSUM value at point 423.

At block 210 of the process 200, a potential fall event is identified ifthe cumulative sum is greater than a cumulative sum threshold value(C_(th)). When the CUSUM is greater than C_(th), then this is indicativethat a potential fall event has occurred.

FIG. 4B shows an example plot of a cumulative sum as a function of time.The CUSUM curve 406 represents the CUSUM of the acceleration data inFIG. 4A across time, where CUSUM is based on acceleration values greaterthan or less than the first acceleration threshold value. As shown inFIG. 4B, the CUSUM curve 406 increases after 2 seconds. Between 2seconds and 3 seconds, the CUSUM curve 406 reaches a value greater than250, where C_(th)=250. After 3 seconds, the CUSUM curve 406 begins totaper and decrease. After 4 seconds, the CUSUM curve 406 reaches zero.

Segmentation can occur at locations where CUSUM equals zero.Specifically, where a CUSUM is zero right before increasing in valuetowards C_(th), that point in time can indicate the start of theduring-fall phase. In addition, where CUSUM is zero after decreasing invalue from C_(th), that point in time can indicate the end of theduring-fall phase. In other words, from where CUSUM reaches C_(th), abeginning of the during-fall phase can be located by tracing backwardsuntil CUSUM is zero, and an end of the during-fall phase can be locatedby tracing forwards until CUSUM is zero. In FIG. 4B, the beginning ofthe during-fall phase can occur around 1.95 seconds and the end of theduring-fall phase can occur around 4.30 seconds. After such points arelocated, a before-fall phase, a during-fall phase, and an after-fallphase can be identified, where the potential fall event can beidentified within the during-fall phase. Not only can the CUSUMalgorithm identify a potential fall event and segment acceleration data,but the CUSUM algorithm can also precisely establish the beginning andend points of the different phases.

FIG. 4C shows an example plot of the acceleration data aftersegmentation. The acceleration data in FIG. 4C may reflect theacceleration values of the user without being processed by a filter,such as a low-pass filter. FIG. 4C shows a before-fall phase on theleft-hand side, where the before-fall phase can occur before about 1.95seconds. FIG. 4C shows a during-fall phase highlighted in the middle,where the during-fall phase can occur between about 1.95 seconds andabout 4.30 seconds. FIG. 4C shows an after-fall phase on the right-handside, where the after-fall phase can occur after about 4.30 seconds. Theplot of the acceleration data has been segmented so that a potentialfall event is indicated in the during-fall phase.

Returning to FIG. 2, in some implementations of the process 200, thecumulative sum for the acceleration data can be determined where a CUSUMvalue increases when an acceleration value is greater than the firstacceleration threshold value (A_(th1)) and where the CUSUM valuedecreases when an acceleration value is less than a second accelerationthreshold value (A_(th2)). While A_(th1) can be the threshold valuebefore CUSUM reaches C_(th), A_(th2) can be the threshold value afterCUSUM reaches C_(th). For example, A_(th2) can be greater than A_(th1)so that the CUSUM decreases more quickly after CUSUM reaches C_(th). Insome implementations, A_(th1) and A_(th2) can be the same. In someimplementations, A_(th1) and A_(th2) can be different.

The CUSUM algorithm can take various formulations, where linearacceleration of the user is cumulatively summed over time and increaseswhere the linear acceleration is greater than a threshold value. By wayof example, a CUSUM algorithm can have the following algorithm:CUSUM[i]=max(0, CUSUM[i−1]+acceleration[i]−A_(th1)), where CUSUM[0]=0,and where A_(th1) is the first acceleration threshold value andacceleration[i] is the linear acceleration magnitude at time [i], andwhere i>0. If CUSUM[i] is greater than C_(th), the CUSUM algorithmdetermined that a fall event has occurred, where C_(th) is thecumulative sum threshold value. In some implementations, after reachingC_(th), the CUSUM algorithm can change to the following algorithm:CUSUM[i] =max(0, CUSUM[i−1]+acceleration[i]−A_(th2)), where A_(th2) isthe second acceleration threshold value.

After the acceleration data has been segmented according to asegmentation algorithm, the duration of each of the phases can beestablished using minimum and maximum time duration. In someimplementations of the process 200, the before-fall phase can beidentified as before the during-fall phase within certain minimum andmaximum time duration. For example, the before-fall phase can beestablished within a time frame of greater than 0 seconds but less thanor equal to 20 seconds, or greater than 1 second but less than or equalto 10 seconds. In some implementations of the process 200, theafter-fall phase can be identified as after the during-fall phase withincertain minimum and maximum time duration. For example, the after-fallphase can be established within a time frame of greater than 1 secondbut less than or equal to 30 seconds, or greater than 5 seconds but lessthan or equal to 20 seconds. In some implementations, the duration ofthe before-fall phase and the duration of the after-fall phase can beestablished using maximum time duration only. This can be used to traceback the beginning point of the before-fall phase from the beginningpoint of the during-fall phase, and trace forward to the end point ofthe after-fall phase from the end point of the during-fall phase.

In some implementations, the process 100 further includes accessingangular velocity data and orientation data from the one or more motionsensors, and segmenting the angular velocity data and the orientationdata. The angular velocity data and the orientation data can besegmented into at least three phases. Such data can be used tosupplement the acceleration data of the user.

Returning to FIG. 1, at block 115 of the process 100, one or morestatistical features are extracted from the segmented acceleration data.Where the acceleration data is segmented into at least three phases, theone or more statistical features can be extracted from at least some ofthe phases. Analysis of the acceleration data can be performedseparately for some of the phases, or for each of the phases, where theanalysis extracts information from the phases and combines theinformation to subsequently determine if a fall event has occurred. Theextracted information can be statistical features, where the one or morestatistical features include one or more of the following: variance,maximum, minimum, 75^(th) percentile, 25^(th) percentile, mean, median,mean crossing rate, absolute area, and combinations thereof. While theextracted statistical features can be extracted statistical features ofthe acceleration data, the extracted statistical features can alsoconstitute statistical features of the angular velocity and/ororientation data from at least some of the phases.

Variance can measure how far a set of acceleration values spread outfrom the mean, and can be the square of the standard deviation. Maximumcan represent the largest acceleration value within a sample. Minimumcan represent the smallest acceleration value within a sample. 75^(th)percentile can represent the acceleration value below which 75% of theacceleration data fall below. 25^(th) percentile can represent theacceleration value below which 25% of the acceleration data fall below.Mean can represent the average acceleration value divided against allacceleration values. Median can represent the middle acceleration valuefrom the list of all acceleration values. Mean crossing rate canrepresent the number of times that the acceleration data crosses themean divided over time. Absolute area can represent the total area undera curve for the acceleration data, which can provide a measure of thesummation of the acceleration values in a particular phase. While theaforementioned statistical features are discussed with reference toacceleration data, it is understood that the statistical features can beapplied to other data, such as angular velocity data and orientationdata.

Some of the aforementioned statistical features can be extracted forparticular phases of the acceleration data, which can provide moreuseful information than if the aforementioned statistical features wereextracted from the entirety of the acceleration data. In someimplementations, a mean crossing rate of the acceleration data isextracted from the during-fall phase. In the duration of a fall event,there can be a lot of fluctuation in linear acceleration. Parts of thebody may be moving rapidly, such as the arms and hands, thereby leadingto the fluctuations in linear acceleration. These parts of the body canbe moving rapidly forwards and backwards. A high value of a meancrossing rate can be indicative of an actual fall event. In someimplementations, a variance of the acceleration data is extracted fromthe after-fall phase. After a fall event, a person may be relativelyimmobile or even unconscious, thereby leading to a generally stableequilibrium of linear acceleration. A low variance can reinforce datasupporting indication of an actual fall event. In some implementations,a maximum acceleration value can be extracted from the during-fallphase. A sufficiently high maximum acceleration value can be indicativeof an actual fall event. In some implementations, an absolute area ofthe acceleration data can be extracted from the after-fall phase. Asufficiently low absolute area can be indicative of an actual fallevent.

In addition to extracting one or more statistical features from theacceleration data, one or more statistical features may be extractedfrom angular velocity data and/or orientation data. After segmentation,useful information can be ascertained from the angular velocity data andorientation data. In some implementations, a variance of angularvelocity can be extracted from the during-fall phase. With rapid changesin angular velocity during a fall, a high variance can be indicative ofan actual fall event. In some implementations, a maximum angularvelocity can be extracted from the during-fall phase. A sufficientlyhigh maximum angular velocity can be indicative of an actual fall event.In some implementations, a minimum angular velocity can be extractedfrom the after-fall phase. A sufficiently low minimum angular velocitycan be indicative of an actual fall event. In some implementations, amedian of orientation can be extracted from the after-fall phase. If theorientation of the user was more horizontal than vertical in theafter-fall phase, then that would be more indicative of an actual fallevent.

In some implementations of the process 100, the duration of time of atleast one of the phases can also be analyzed. The duration of time of atleast one of the phases can be calculated after segmenting theacceleration data. For example, the duration of time of the during-fallphase in FIG. 4C is calculated to be about 2.35 seconds. In someimplementations, the duration of time of the during-fall phase can becompared to a minimum threshold value and a maximum threshold value. Theduring-fall phase may not be too short compared to an actual fall, so asto distinguish from abbreviated activities such as an abrupt movement ormovements. The during-fall phase may not be too long compared to anactual fall, so as to distinguish from ongoing activities such asexercise. In fact, actual fall events usually encompass a specific timeframe, such as between about 0.25 seconds and about 15 seconds, orbetween about 0.5 seconds and about 5 seconds. In some implementations,if the duration of time of the during-fall phase is not within theminimum threshold value and the maximum threshold value, then thepotential fall event can be rejected.

At block 120 of the process 100, a determination is made whether apotential fall event is a fall event based at least in part on the oneor more extracted statistical features. If the extracted statisticalfeatures are indicative of a fall event, then the potential fall eventis a fall event. Otherwise, the potential fall event can be an activityof daily life. In some implementations, determining whether a potentialfall event is a fall event is based on the one or more extractedstatistical features and a classifier trained using a machine-learningalgorithm. A machine-learning algorithm can be used to train aclassifier to classify events as actual falls or activities of dailyliving (i.e., non-falls). In some implementations, determining whether apotential fall event is a fall event is described further in a process300 in FIG. 3.

FIG. 3 shows a flow chart illustrating an example process fordetermining whether a potential fall event is a fall event based atleast in part on one or more extracted statistical features according tosome implementations. The process 300 may be performed in a differentorder or with different, fewer, or additional operations. In someimplementations, the blocks of the process 300 may be performed by adevice shown in FIG. 5, or by a device shown in FIG. 6, as describedbelow. In some implementations, the blocks of the process 300 may beimplemented, at least in part, according to software stored on one ormore non-transitory computer readable media.

An example classifier of the process 300 predicts whether a potentialfall event is a fall event based at least in part on the one or moreextracted statistical features. At block 305 of the process 300, the oneor more extracted statistical features from at least some of the phasesare aggregated. Each of the extracted statistical features contributesto jointly determine whether a potential fall event is a fall event.

In some implementations, aggregation of the one or more extractedstatistical features can include calculating a metric based on the oneor more extracted statistical features. In some implementations,calculating the metric comprises a linear combination of the extractedstatistical features. In some implementations, calculating the metriccomprises a non-linear combination of the extracted statisticalfeatures. In some implementations, coefficients can be assigned to eachof the extracted statistical features in the metric so that certainstatistical features are more weighted than others. A classifier can betrained to classify whether a fall event has occurred from data (e.g.,acceleration data, angular velocity data, or orientation data) of aplurality of different fall events and/or a plurality of differentnon-fall events. In other words, previous fall events and non-fallevents can be used to train the classifier. The classifier can betrained to classify fall events using a suitable algorithm from amongdecision trees, random forest, boosting, support vector machines, neuralnetworks, logistic regression, etc. In some implementations, theclassifier can be trained to classify fall events using logisticregression. Coefficients to statistical features can be assigned basedon the sensitivity of statistical features, where some statisticalfeatures may be more sensitive to detecting falls than others based onthe different fall events and non-fall events. The metric can becalculated as a linear or non-linear combination of the extractedstatistical features weighted by the assigned coefficients. Thecalculated metric can serve as an input for the classifier. Theclassifier can use a suitable algorithm such as logistic regression toclassify whether the potential fall event is a fall event.

At block 310 of the process 300, the potential fall event is classifiedas a fall event based at least in part on the one or more extractedstatistical features using a classifier trained by a machine-learningalgorithm. The process 300 at block 310 uses the classifier to classifya potential fall event as either an actual fall event or an activity ofdaily living. In some implementations, the classifier uses logisticregression to predict whether the one or more extracted statisticalfeatures of the potential fall event sufficiently show that a fall eventhas occurred or not. In some implementations, the one or more extractedstatistical features of the potential fall event can be indicative ofthe severity of the fall. In some implementations, if the classifierclassifies the potential fall event as a fall event, an alarm signal canbe generated.

FIG. 5 shows a block diagram representation of components of an examplefall detection device according to some implementations. As with otherimplementations disclosed herein, the number of elements and types ofelements shown in FIG. 5 are merely by way of example. Otherimplementations may have more, fewer, or different elements. In theimplementation in FIG. 5, the fall detection device 500 includes amotion sensor system 510, filter 520, a processor 530, a memory 540, aradio-frequency (RF) unit 550 coupled to an antenna 552, and a powersupply 560.

In some implementations, the motion sensor system 510 includes one ormore motion sensors 512, 514, and 516. The one or more motion sensorscan include one or more accelerometers 512 that may be used to obtainacceleration data of a user. Optionally, the motion sensor system 510can include one or more gyroscopes 514 and one or more magnetometers516. The one or more gyroscopes 514 can provide measurements of angularacceleration, angular velocity, or rotation. The one or moremagnetometers 516 can provide measurements of magnetic field or force.One or both of the gyroscopes 514 and the magnetometers 516 can providethe orientation of the user. Additionally, the one or more of thesensors 512, 514, and 516 can provide other motion-related informationof the user, such as the type of motion of the user, the direction ofmotion of the user, the position of the fall detection device 500,instantaneous and average velocities and accelerations of the user, etc.In some implementations, the motion sensor system 510 includes all ofthe motion sensors 512, 514, and 516 described above. In some otherimplementations, the motion sensor system 510 includes a subset of themotion sensors 512, 514, and 516 described above. For example, themotion sensor system 510 may include only the one or more accelerometers512 to reduce power consumption in the fall detection device 500.

The motion sensor system 510 may access acceleration data of the userand pass the acceleration data through a filter 520. The filter 520 canremove certain frequencies that fall outside a desired frequency rangeso that the acceleration data includes acceleration data encompassingnormal activities and falls. In some implementations, the filter 520 canbe a low-pass filter to remove high-frequency signals and smooth out theacceleration data. In addition, the filter 520 can serve to removegravity and noise.

The motion sensor system 510 and the filter 520 may be coupled to theprocessor 530 so that the processor 530 may control or receiveacceleration data. In some implementations, the processor 530 maycontrol or receive additional data from the motion sensor system 510,such as angular velocity data and orientation data. The processor 530may be dedicated hardware specifically adapted to perform a variety offunctions for the fall detection device 500. In some implementations,the processor 530 may be or include a programmable processing unit 532that may be programmed with processor-executable instructions. In someimplementations, the processor 530 may be a programmable microprocessor,microcomputer, or multiple processor chip or chips that can beconfigured by software instructions to perform a variety of functionsfor the fall detection device 500. In some implementations, theprocessor 530 may be a combination of dedicated hardware and aprogrammable processing unit 532.

The processor 530 may be configured to access and process output signalsfrom the motion sensor system 510 and the filter 520, such asacceleration data of the user. Using the acceleration data, theprocessor 530 may be configured to determine if a fall event hasoccurred. The processor 530 may perform calculations to segment theacceleration data using a segmentation algorithm. The processor 530 mayfurther perform calculations using a CUSUM algorithm to segment theacceleration data and identify a potential fall event. The processor 530may also extract and aggregate statistical features from the segmentedacceleration data, and apply a classifier to determine if an actual fallevent has occurred based on the extracted statistical features. Theclassifier may be trained using a machine-learning algorithm.

Various operations of the processes 100, 200, and 300 may be performedby the fall detection device 500. In some examples, one or more of theprocesses 100, 200, and 300 may be performed by the fall detectiondevice 500 that includes the processor 530. In some implementations, theprocessor 530 can be coupled to the one or more motion sensors 512, 514,and 516 and can analyze the acceleration data. The processor 530 can beconfigured to: segment the acceleration data to identify a potentialfall event, extract one or more statistical features from the segmentedacceleration data, and determine whether a potential fall event is afall event based at least in part on the one or more statisticalfeatures. In segmenting the acceleration data, the processor 530 can beconfigured to determine a cumulative sum for the acceleration data,where the cumulative sum is based on acceleration value being greaterthan or less than a first acceleration threshold value, and identify apotential fall event if the cumulative sum is greater than thecumulative sum threshold value. By way of example, the cumulative sumcan be calculated using a CUSUM algorithm: CUSUM[i]=max(0,acceleration[i]+CUSUM[i−1]−A_(th1)), where CUSUM[0]=0, and where A_(th1)is the first acceleration threshold value and acceleration[i] is theacceleration value at time [i], and where i>0. In some implementations,after the cumulative sum reaches a cumulative sum threshold value atC_(th), A_(th1) can be replaced by a second acceleration threshold value(A_(th2)).

In implementations for determining whether a potential fall event is afall event based on the one or more statistical features, the processor530 for analyzing the acceleration data can be configured to aggregatethe one or more extracted statistical features from at least some of thephases, and classify the potential fall event as a fall event based atleast in part on the one or more extracted statistical features using aclassifier trained by a machine-learning algorithm. In someimplementations, the processor 530 can be configured to classify thepotential fall event as a fall event using a classifier trained usinglogistic regression. In some implementations, the processor 530 can beconfigured to calculate a metric using a linear or non-linearcombination of the extracted statistical features weighted by assignedcoefficients. The calculated metric can serve as an input for theclassifier.

In some implementations, a memory 540 may store processor-executableinstructions and/or outputs from the motion sensor system 510. In someimplementations, the memory 540 may be a volatile memory, non-volatilememory (e.g., flash memory), or a combination thereof. In someimplementations, the memory 540 may include internal memory included inthe processor 530, memory external to the processor 530, or acombination thereof. The memory 540 may be coupled to the processor 530.In some implementations, the memory 540 may store data related to thesegmentation algorithm and the machine-learning algorithm. For example,the processor 530 may access data regarding the first accelerationthreshold value, the second acceleration threshold value, and thecumulative sum threshold value stored in the memory 540. The processor530 may also store data in the memory 540 regarding the accelerationdata and the extracted statistical features of the user for training theclassifier using a machine-learning algorithm.

In some implementations, the processor 530 may be coupled to an RF unit550 in order to communicate an alarm signal if a fall event has beendetermined. Upon determining that a potential fall event is a fallevent, an alarm signal can be generated in the processor 530 and sent tothe RF unit 550. In some implementations, the alarm signal can becommunicated to a remote device for alerting a call center or caregiver.In some implementations, the RF unit 550 may be a transmitter-only or atwo-way transceiver. The RF unit 550 may operate in one or morefrequency bands depending on the supported type of communications.

In some implementations, one or more of the motion sensor system 510,the filter 520, the processor 530, the memory 540, the RF unit 550, andany other electronic components of the fall detection device 500 may bepowered by the power supply 560. The power supply 560 may be a battery,a solar cell, and other suitable power sources for harvesting power.

FIG. 6 shows a block diagram representation of a processor in a falldetection device and associated steps performed by the processoraccording to some implementations. The fall detection device 600 caninclude at least a processor 630, where the processor 630 can controlseveral of the operations of the fall detection device 600. The falldetection device 600 can include one or more additional components asdescribed in the fall detection device 500 of FIG. 5, such as one ormore motion sensors 512, 514, and 516, filter 520, memory 540, RF unit550 coupled to antenna 552, and power supply 560.

The processor 630 may be a combination of dedicated hardware, such asfilters, gates, analog-to-digital conversion, etc., and a processingunit 632 configured to perform various operations. The processing unit632 may be programmed with processor-executable instructions. In someimplementations, the processing unit 632 can be configured by softwareinstructions to perform a variety of functions for the fall detectiondevice 600.

Various illustrative logics, logical blocks, modules, circuits, andalgorithm steps described in connection with the implementationsdisclosed herein may be implemented as electronic hardware, computersoftware, or combinations of both. The interchangeability of hardwareand software has been described generally, in terms of functionality,and illustrated in the various illustrative components, blocks, modules,circuits, and steps described above. The interchangeability of hardwareand software can also be described, in terms of functionality, andillustrated in the various illustrative components, blocks, modules,circuits, and steps 634 a, 634 b, and 634 c. Whether such functionalityis implemented in hardware or software depends on the particularapplication and design constraints imposed on the system or device.

As shown in FIG. 6, acceleration data is received by the processor 630.In some implementations, the acceleration data can be filtered andnormalized. The acceleration data can be received from one or moremotion sensors, including but not limited to one or more accelerometers.The processing unit 632 processes the acceleration data through a seriesof logics, logical blocks, modules, circuits, and/or algorithm stepsshown in blocks 634 a, 634 b, and 634 c. At segmentation block 634 a,the processing unit 632 segments the acceleration data into at leastthree phases to identify a potential fall event, where the at leastthree phases include but are not limited to (1) a before-fall phase, (2)a during-fall phase, and (3) an after-fall phase, where the during-fallphase includes the potential fall event. In some implementations, thesegmentation block 634 a applies a segmentation algorithm to segment theacceleration data. The segmentation algorithm can include a CUSUMalgorithm, where the CUSUM algorithm can be used to identify thepotential fall event and segment the acceleration data. At extractionblock 634 b, the processing unit 632 extracts one or more statisticalfeatures from at least some of the phases or each of the phases.Examples of statistical features include: variance, maximum, minimum,75^(th) percentile, 25^(th) percentile, mean, median, mean crossingrate, absolute area, or any combination thereof. In someimplementations, the acceleration data in each of the phases can beanalyzed to extract statistical features from each of the phases. Atclassification block 634 c, the processing unit 632 classifies thepotential fall event as a fall event based on an aggregation of theextracted statistical features and a classifier. A machine-learningalgorithm can train the classifier by applying a suitable algorithm suchas logistic regression, where the classifier can determine whether thecandidate fall event is an actual fall event. In some implementations, ametric can be calculated based on the extracted statistical features,where the calculated metric includes a linear or non-linear combinationof the extracted statistical features weighted by assigned coefficients.After the classification determines that a fall event has occurred, analarm signal may be generated. In some implementations, the alarm signalmay be sent to an RF unit for communicating to an external device.

The hardware and data processing apparatus used to implement the variousillustrative logics, logical blocks, modules, and circuits described inconnection with the aspects disclosed above and disclosed in blocks 634a, 634 b, and 634 c may be implemented or performed with a generalpurpose single- or multi-chip processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the function described herein.The processing unit 632 may be a microprocessor, or, any conventionalprocessor, controller, microcontroller, or state machine. The processingunit 632 also may be implemented as a combination of computing devices,such as a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors with a DSP core, or anyother such configuration. In some implementations, particular steps andmethods may be performed by circuitry that is specific to a givenfunction.

In one or more aspects, the functions described may be implemented inhardware, digital electronic circuitry, computer software, firmware,including the structures disclosed in this specification and theirstructural equivalents, or in any combination thereof. Implementationsof the subject matter described in this specification also can beimplemented as one or more computer programs, i.e., one or more modulesof computer program instructions, encoded on a computer storage mediafor execution by, or to control the operation of, data processingapparatus.

If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. The steps of a method or algorithm disclosedherein may be implemented in a processor-executable software modulewhich may reside on a computer-readable medium. Computer-readable mediaincludes both computer storage media and communication media includingany medium that can be enabled to transfer a computer program from oneplace to another. A storage media may be any available media that may beaccessed by a computer. By way of example, and not limitation, suchcomputer-readable media may include RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that may be used to store desired programcode in the form of instructions or data structures and that may beaccessed by a computer. Also, any connection can be properly termed acomputer-readable medium. Disk and disc, as used herein, includescompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above also may be included within the scope ofcomputer-readable media. Additionally, the operations of a method oralgorithm may reside as one or any combination or set of codes andinstructions on a machine readable medium and computer-readable medium,which may be incorporated into a computer program product.

Various modifications to the implementations described in thisdisclosure may be readily apparent to those skilled in the art, and thegeneric principles defined herein may be applied to otherimplementations without departing from the spirit or scope of thisdisclosure. Thus, the claims are not intended to be limited to theimplementations shown herein, but are to be accorded the widest scopeconsistent with this disclosure, the principles and the novel featuresdisclosed herein.

Additionally, certain features that are described in this specificationin the context of separate implementations also can be implemented incombination in a single implementation. Conversely, various featuresthat are described in the context of a single implementation also can beimplemented in multiple implementations separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Further, the drawings may schematically depict one more exampleprocesses in the form of a flow diagram. However, other operations thatare not depicted can be incorporated in the example processes that areschematically illustrated. For example, one or more additionaloperations can be performed before, after, simultaneously, or betweenany of the illustrated operations. Moreover, various ones of thedescribed and illustrated operations can itself include and collectivelyrefer to a number of sub-operations. For example, each of the operationsdescribed above can itself involve the execution of a process oralgorithm. Furthermore, various ones of the described and illustratedoperations can be combined or performed in parallel in someimplementations. Similarly, the separation of various system componentsin the implementations described above should not be understood asrequiring such separation in all implementations. As such, otherimplementations are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results.

What is claimed is:
 1. A method of identifying a fall event, comprising:accessing acceleration data of an electronic device from one or moremotion sensors; segmenting the acceleration data to identify a potentialfall event, comprising: determining a cumulative sum for theacceleration data; and identifying the potential fall event if thecumulative sum is greater than a cumulative sum threshold value;extracting one or more statistical features from the segmentedacceleration data; and determining whether the potential fall event is afall event based at least in part on the one or more statisticalfeatures.
 2. The method of claim 1, wherein segmenting the accelerationdata includes segmenting the acceleration data into at least threephases, wherein the at least three phases include (i) a before-fallphase, (ii) a during-fall phase, and (iii) an after-fall phase, andwherein the during-fall phase includes the potential fall event.
 3. Themethod of claim 2, further comprising: analyzing the duration of time ofthe during-fall phase, wherein determining whether the potential fallevent is a fall event is based at least in part on the duration of timeof the during-fall phase.
 4. The method of claim 2, wherein the one ormore statistical features include one or more of the following:variance, maximum, minimum, 75^(th) percentile, 25^(th) percentile,mean, median, mean crossing rate, absolute area, or any combinationthereof.
 5. The method of claim 4, wherein the one or more statisticalfeatures include: a mean crossing rate of acceleration extracted fromthe during-fall phase and variance of acceleration extracted from theafter-fall phase.
 6. The method of claim 4, further comprising:accessing angular velocity data and orientation data from the one ormore motion sensors; and segmenting the angular velocity data and theorientation data into the at least three phases, wherein the one or morestatistical features include: a mean crossing rate of accelerationextracted from the during-fall phase, a maximum acceleration extractedfrom the during-fall phase, an absolute area of acceleration extractedfrom the after-fall phase, a variance of angular velocity extracted fromthe during-fall phase, a maximum of angular velocity extracted from theduring-fall phase, a minimum of angular velocity extracted from theafter-fall phase, a median of orientation extracted from the after-fallphase, or any combination thereof.
 7. The method of claim 1, wherein theelectronic device is a wearable device.
 8. The method of claim 1,wherein the one or more motion sensors consist of one or moreaccelerometers.
 9. The method of claim 1, wherein the cumulative sum isbased on acceleration values greater than or less than a firstacceleration threshold value.
 10. The method of claim 9, wherein thefirst acceleration threshold value and the cumulative sum thresholdvalue are identified by a machine-learning algorithm.
 11. The method ofclaim 1, wherein the cumulative sum has the formula: CUSUM[i]=max(0,CUSUM[i−1]+acceleration[i]−A_(th1)), wherein CUSUM[0]=0, and whereinA_(th1) is the first acceleration threshold value and acceleration[i] isan acceleration value at time [i], and wherein i>0.
 12. The method ofclaim 1, wherein determining whether a potential fall event is a fallevent comprises: aggregating the one or more statistical features fromthe segmented acceleration data; calculating a metric based on the oneor more statistical features; and classifying the potential fall eventas a fall event based at least in part on the calculated metric.
 13. Themethod of claim 12, wherein the metric is calculated as a linear ornon-linear combination of the extracted statistical features weighted bycoefficients.
 14. The method of claim 12, wherein the potential fallevent is classified using a classifier trained by a machine-learningalgorithm.
 15. An electronic device for identifying a fall event,comprising: one or more motion sensors configured to access accelerationdata of the electronic device; a processor coupled to the one or moremotion sensors, the processor configured to: segment the accelerationdata to identify a potential fall event; extract one or more statisticalfeatures from the segmented acceleration data; and determine whether thepotential fall event is a fall event based at least in part on the oneor more statistical features, wherein the processor configured tosegment the acceleration data is configured to determine a cumulativesum for the acceleration data, and identify a potential fall event ifthe cumulative sum is greater than a cumulative sum threshold value. 16.The electronic device of claim 15, wherein the processor configured tosegment the acceleration data is configured to segment the accelerationdata into at least three phases, wherein the at least three phasesinclude (i) a before-fall phase, (ii) a during-fall phase, and (iii) anafter-fall phase, and wherein the during-fall phase includes thepotential fall event.
 17. The electronic device of claim 16, wherein theone or more statistical features include: a mean crossing rate ofacceleration extracted from the during-fall phase and variance ofacceleration extracted from the after-fall phase.
 18. The electronicdevice of claim 15, wherein the electronic device is a wearable deviceand the one or more motion sensors consist of one or moreaccelerometers.
 19. The electronic device of claim 15, wherein thecumulative sum has the formula: CUSUM[i]=max(0,CUSUM[i−1]+acceleration[i]−A_(th1)), wherein CUSUM[0]=0, and whereinA_(th1) is the first acceleration threshold value and acceleration[i] isan acceleration value at time [i], and wherein i>0.
 20. The electronicdevice of claim 15, further comprising: a low-pass filter configured tosmooth out the acceleration data accessed from the one or more motionsensors.
 21. An electronic device for identifying a fall event,comprising: means for accessing acceleration data; and means foranalyzing the acceleration data coupled to the means for accessingacceleration data, the means for analyzing the acceleration dataconfigured to: segment the acceleration data to identify a potentialfall event; extract one or more statistical features from the segmentedacceleration data; and determine whether the potential fall event isfall event based at least in part on the one or more statisticalfeatures, wherein the means for analyzing the acceleration dataconfigured to segment the acceleration data is configured to determine acumulative sum for the acceleration data, and identify a potential fallevent if the cumulative sum is greater than a cumulative sum thresholdvalue.
 22. The electronic device of claim 21, wherein the means foranalyzing the acceleration data configured to segment the accelerationdata is configured to segment the acceleration data into at least threephases, wherein the at least three phases include (i) a before-fallphase, (ii) a during-fall phase, and (iii) an after-fall phase, andwherein the during-fall phase includes the potential fall event, andwherein the one or more statistical features include: a mean crossingrate of acceleration extracted from the during-fall phase and varianceof acceleration extracted from the after-fall phase.
 23. The electronicdevice of claim 21, wherein the electronic device is a wearable deviceand the one or more motion sensors consist of one or moreaccelerometers.